Histopathology Image-Based Cancer Classification Utilizing Transfer Learning Approach

The primary objective of medicine and technology is to provide services capable of identifying and treating patients based on their particular conditions. The accuracy of disease diagnosis is of paramount importance to this endeavor. Cancer is a major cause of death on a global scale, with prognosis, prevention, and prompt intervention offering the possibility of complete patient remission. In this Python-based experiment, we investigate the viability of transfer learning for lymph node diagnosis in histopathology images, which are used to fine-tune a DenseNet121-based pre-trained model. The results indicate that fine-tuning the DenseNet121 model is more effective than training the model from start. Due to variations in the network’s initial weight distribution, the network’s average accuracy is 96%. Following the implementation and completion of ten training epochs, the average overall accuracy reaches a maximum of 98%, with less than 10% error accuracy.